The cloud is getting smarter by the minute. In fact, it will soon know more about the photos you’ve uploaded than you do.

Cloud storage company Box announced today that it is adding computer-vision technology from Google to its platform. Users will be able to search through photos, images, and other documents using their visual components, instead of by file name or tag. “As more and more data goes into the cloud, we’re seeing they need more powerful ways to organize and understand their content,” says CEO Aaron Levie.

Computer-vision technology has improved remarkably over the past few years thanks to a machine-learning approach known as deep learning (see “10 Breakthrough Technologies 2013: Deep Learning”). A deep neural network—loosely inspired by the way neurons process and store information—can learn to recognize categories of objects, such as a “red sweater” or a “pickup truck.” Ongoing research, including work from Google’s researchers, is improving the ability of algorithms to describe what’s happening in images.

Box’s computer-vision feature could be a good way for companies to dip their toes into AI and machine learning. It removes the need to manually annotate thousands of images, and it will make it possible to search through older files in ways that might not have occurred to anyone during tagging. Levie says one company testing the technology is using it to search images for particular people.

The announcement is the latest sign that cloud computing is being reinvented through machine learning and artificial intelligence. AI is already the weapon of choice in the battle to dominate cloud computing, with companies that offer on-demand computing—Google, Amazon, and Microsoft among them—all increasingly touting added machine-learning features.

Fei-Fei Li, chief scientist of Google Cloud and a professor at Stanford University who specializes in computer vision and machine learning, said in a statement that the announcement shows how broadly available AI technology is becoming. “Ultimately it will democratize AI for more people and businesses,” Li said.

Levie says his company is looking at adding machine learning for other types of content. This could include audio and video, but also text, for which an algorithm could add semantic analysis, making it possible to search by the meaning of a document rather than specific keywords.

It’s also significant that Box is relying on computer vision from Google, rather than technology developed in-house. This reflects the fact that a few big players have come to dominate the more fundamental aspects of AI like computer vision, voice recognition, and natural-language processing. “If you think about the strength that Google has in image recognition, it would just be strategically unwise for us to try to compete with them,” Levie says. He says his company’s researchers are exploring ways of applying machine learning to the behavior of its customers. This process might reveal ways to optimize the Box service, or help identify tasks that could be ripe for automation, Levie says.

Google’s Cloud Vision API can recognize many thousands of everyday objects in images. However, some customers might need the ability to recognize and search through specific types of images, for example medical or architectural images. So Box’s researchers are exploring ways for customers to train their own vision systems if necessary.

Fasten your harnesses, because the era of cloud computing’s giant data centers is about to be rear-ended by the age of self-driving cars. Here’s the problem: When a self-driving car has to make snap decisions, it needs answers fast. Even slight delays in updating road and weather conditions could mean longer travel times or dangerous errors. But those smart vehicles of the near-future don’t quite have the huge computing power to process the data necessary to avoid collisions, chat with nearby vehicles about optimizing traffic flow, and find the best routes that avoid gridlocked or washed-out roads. The logical source of that power lies in the massive server farms where hundreds of thousands of processors can churn out solutions. But that won’t work if the vehicles have to wait the 100 milliseconds or so it usually takes for information to travel each way to and from distant data centers. Cars, after all, move fast.

That problem from the frontier of technology is why many tech leaders foresee the need for a new “edge computing” network—one that turns the logic of today’s cloud inside out. Today the $247 billion cloud computing industry funnels everything through massive centralized data centers operated by giants like Amazon, Microsoft, and Google. That’s been a smart model for scaling up web search and social networks, as well as streaming media to billions of users. But it’s not so smart for latency-intolerant applications like autonomous cars or mobile mixed reality.

“It’s a foregone conclusion that giant, centralized server farms that take up 19 city blocks of power are just not going to work everywhere,” says Zachary Smith, a double-bass player and Juilliard School graduate who is the CEO and cofounder of a New York City startup called Packet. Smith is among those who believe that the solution lies in seeding the landscape with smaller server outposts—those edge networks—that would widely distribute processing power in order to speed its results to client devices, like those cars, that can’t tolerate delay.

Packet’s scattered micro datacenters are nothing like the sprawling facilities operated by Amazon and Google, which can contain tens of thousands of servers and squat outside major cities in suburbs, small towns, or rural areas, thanks to their huge physical footprints and energy appetites. Packet’s centers often contain just a few server racks—but the company promises customers in major cities speedy access to raw computing power, with average delays of just 10 to 15 milliseconds (an improvement of roughly a factor of ten). That kind of speed is on the “must have” lists of companies and developers hoping to stream virtual reality and augmented reality experiences to smartphones, for example. Such experiences rely upon a neurological process—the vestibulo-ocular reflex—that coordinates eye and head movements. It occurs within seven milliseconds, and if your device takes 10 times that long to hear back from a server, forget about suspension of disbelief.

Immersive experiences are just the start of this new kind of need for speed. Everywhere you look, our autonomously driving, drone-clogged, robot-operated future needs to shave more milliseconds off its network-roundtrip clock. For smart vehicles alone, Toyota noted that the amount of data flowing between vehicles and cloud computing services is estimated to reach 10 exabytes per month by 2025.

Cloud computing giants haven’t ignored the lag problem. In May, Microsoft announced the testing of its new Azure IoT Edge service, intended to push some cloud computing functions onto developers’ own devices. Barely a month later, Amazon Web Services opened up general access to AWS Greengrass software that similarly extends some cloud-style services to devices running on local networks. Still, these services require customers to operate hardware on their own. Customers who are used to handing that whole business off to a cloud provider may view that as a backwards step.

US telecom companies are also seeing their build-out of new 5G networks—which should eventually support faster mobile data speeds—as a chance to cut down on lag time. As the service providers expand their networks of cell towers and base stations, they could seize the opportunity to add server power to the new locations. In July, AT&T announced plans to build a mobile edge computing network based on 5G, with the goal of reaching “single-digit millisecond latency.” Theoretically, data would only need to travel a few miles between customers and the nearest cell tower or central office, instead of hundreds of miles to reach a cloud data center.

“Our network consists of over 5,000 central offices, over 65,000 cell towers, and even several hundred thousand distribution points beyond that, reaching into all the neighborhoods we serve,” says Andre Fuetsch, CTO at AT&T. “All of a sudden, all those physical locations become candidates for compute.”

AT&T claims it has a head start on rival telecoms because of its “network virtualization initiative,” which includes the software capability to automatically juggle workloads and make good use of idle resources in the mobile network, according to Fuetsch. It’s similar to how big data centers use virtualization to spread out a customer’s data processing workload across multiple computer servers.

Meanwhile, companies such as Packet might be able to piggyback their own machines onto the new facilities, too. ”I think we’re at this time where a huge amount of investment is going into mobile networks over the next two to three years,” Packet’s Smith says. “So it’s a good time to say ‘Why not tack on some compute?’” (Packet’s own funding comes in part from the giant Japanese telecom and internet conglomerate Softbank, which invested $9.4 million in 2016.) In July 2017, Packet announced its expansion to Ashburn, Atlanta, Chicago, Dallas, Los Angeles, and Seattle, along with new international locations in Frankfurt, Toronto, Hong Kong, Singapore, and Sydney.

Packet is far from the only startup making claims on the edge. Austin-based Vapor IO has already begun building its own micro data centers alongside existing cell towers. In June, the startup announced its “Project Volutus” initiative, which includes a partnership with Crown Castle, the largest US provider of shared wireless infrastructure (and a Vapor IO investor). That enables Vapor IO to take advantage of Crown Castle’s existing network of 40,000 cell towers and 60,000 miles of fiber optic lines in metropolitan areas. The startup has been developing automated software to remotely operate and monitor micro data centers to ensure that customers don’t experience interruptions in service if some computer servers go down, says Cole Crawford, Vapor IO’s founder and CEO.

Don’t look for the edge to shut down all those data centers in Oregon, North Carolina, and other rural outposts: Our era’s digital cathedrals are not vanishing anytime soon. Edge computing’s vision of having “thousands of small, regional and micro-regional data centers that are integrated into the last mile networks” is actually a “natural extension of today’s centralized cloud,” Crawford says. In fact, the cloud computing industry has extended its tentacles toward the edge with content delivery networks such as Akamai, Cloudflare, and Amazon CloudFront that already use “edge locations” to speed up delivery of music and video streaming.

Nonetheless, the remote computing industry stands on the cusp of a “back to the future” moment, according to Peter Levine, general partner at the venture capital firm Andreessen Horowitz. In a 2016 video presentation, Levine highlighted how the pre-2000 internet once relied upon a decentralized network of PCs and client servers. Next, the centralized network of the modern cloud computing industry really took off, starting around 2005. Now, demand for edge computing is pushing development of decentralized networks once again (even as the public cloud computing industry’s growth is expected to peak at 18 percent this year, before starting to taper off).

That kind of abstract shift is already showing up, unlocking experiences that could only exist with help from the edge. Hatch, a spinoff company from Angry Birds developer Rovio, has begun rolling out a subscription game streaming service that allows smartphone customers to instantly begin playing without waiting on downloads. The service offers low-latency multiplayer and social gaming features such as sharing gameplay via Twitch-style live-streaming. Hatch has been cagey about the technology it developed to slash the number of data-processing steps in streaming games, other than saying it eliminates the need for video compression and can do mobile game streaming at 60 frames per second. But when it came to figuring out how to transmit and receive all that data without latency wrecking the experience, Hatch teamed up with—guess who—Packet.

“We are one of the first consumer-facing use cases for edge computing,” says Juhani Honkala, founder and CEO of Hatch. “But I believe there will be other use cases that can benefit from low latency, such as AR/VR, self-driving cars, and robotics.”

Of course, most Hatch customers will not know or care about how those micro datacenters allow them to instantly play games with friends. The same blissful ignorance will likely surround most people who stream augmented-reality experiences on their smartphones while riding in self-driving cars 10 years from now. All of us will gradually come to expect new computer-driven experiences to be made available anywhere instantly—as if by magic. But in this case, magic is just another name for putting the right computer in the right place at the right time.

“There is so much more that people can do,” says Packet’s Smith, “than stare at their smartphones and wait for downloads to happen.” We want our computation now. And the edge is the way we’ll get it.

One of the barriers for enterprises storing data in the cloud is data migration, a process that has traditionally been slow and costly, hindered by network limitations. IBM wants to remove this barrier for its customers with a new cloud migration solution designed for moving massive amounts of data to the cloud.

IBM Cloud Mass Data Migration is a shippable storage device, which offers 120 TB and uses AES 256-bit encryption. The device also uses RAID-6 to ensure data integrity, and is shock-proof. The device is a flat-rate, and includes overnight round-trip shipping.

The device is about the size of a suitcase, and has wheels so it can be easily moved around a data center, Michael Fork, distinguished engineer and director, cloud infrastructure, IBM Watson and cloud platform said. Fork said that the solution allows customers to migrate 120 TB in seven days.

“When you actually look at the networking aspects of this, for example if you were to transfer 120TB over a 100 Mbps internet connection, that would take 100 or more days,” he said.

Similar options on the market include the AWS Snowball Edge, which was launched last year and offers 100 TB of usable storage capacity. In June, Google introduced Transfer Appliance, which offers up to 480TB in 4U or 100TB in 2U of raw data capacity. In the chart below, Google broke down how long data transfer can take over different connections.

“Previously we supported two main transfer methods. One was an IBM solution called IBM Data Transfer service, and this allows you to ship us a USB hard drive or CD/DVD, and so you could migrate in up to 10 TBs of data pretty easily using that service,” Fork said. “The other solution IBM supports is through IBM Aspera, a network-based transfer.”

IBM Cloud Mass Data Migration is designed for any customer that has large amounts of data to migrate to IBM Cloud, Fork said, pointing to customers who move large SAP datasets or datasets for use with IBM Watson or other cognitive services.

“VMware customers are bringing to IBM Cloud large amounts of data, VMDKs, machine images, they need a fast and efficient way to move large amounts of those,” he said.

Beyond Lights-Out: Future Data Centers Will Be Human-FreeA new generation of data centers will be optimized for extreme efficiency, not for human access or comfort.Critical Thinking, a weekly column on innovation in data center infrastructure. More about the column and the author here.

The idea of a “lights-out” data center is not new, but it is evolving. Operators such as Hewlett Packard Enterprise and AOL have been long-term proponents of remote monitoring and management to reduce, or entirely replace, the need for dedicated on-site staff. The most well-known current advocate is probably colocation provider EdgeConneX that has integrated a lights-out approach into the fabric of its business.

However, despite the efficiency benefits, lights-out, or “dark,” sites are still viewed with skepticism in some quarters; not having staff readily on-hand to deal with outages is deemed just too high-risk. Data center certification body Uptime Institute, for example, recommends that one to two qualified staff are needed on-site at all times to support the safe operation of a Tier III or IV facility.But while lights-out may be a niche option now, developments in remote monitoring, analytics, AI, and robotics could eventually see it taken much further.

These technologies combined with the elimination of all concessions to human comfort will enable ever more efficient and available data centers, some experts argue. Technology analyst firm 451 Research recently coined the phrase “ Datacenter as a Machine” (subscription required) to define unstaffed facilities that are primarily designed, built, and operated as units of IT rather than buildings. “As data centers become more complex, with tighter software-controlled integration between components, they will increasingly be viewed as complex machines rather than real estate,” the analyst group argues.

A facility designed and optimized exclusively for IT, rather than human operators, could enjoy a range of advantages over more conventional sites:

Improved cooling efficiency: There is good evidence that facilities could be operated at higher temperatures and humidity without impacting the reliability and performance of IT equipment. Progressive operators have made efforts to move into the upper reaches of ASHRAE’s recommended, or even allowable, temperature ranges. But the approach isn’t more pervasive due in part to its impact on human comfort. IT equipment may be functional at 80F and up, but it’s not a pleasant working environment for staff. Other highly efficient forms of cooling could make things even more uncomfortable. For example, close-coupled cooling technologies, such as direct liquid immersion, capture more than 90 percent of the IT heat load in a dielectric fluid but make no concession for the human operator. For the technology to become widely deployed in conventional sites additional, inefficient, perimeter cooling would be required in some locations just to keep the operators cool.

Better capacity management: Everything from rack height to access-aisle width is designed to make it easier for staff to install and maintain equipment rather than to optimize for efficiency. But if this space requirement was eliminated, equipment (power and cooling permitting) could be fitted into a much smaller footprint with, for example, potentially much higher, robot-accessible racks.

Reduced downtime and improved safety: According to a 2016 study by the Ponemon Institute, human error was the second-highest cause (behind power chain failures) of data center downtime. Electrocution – via arc-flash or other causes – also remains a real and present threat without the correct safety precautions. Use of hypoxic fire suppression – lowering oxygen levels – also has benefits for fire safety but again makes for a difficult working environment. A facility that was essentially off-limits to all but periodic or emergency access by qualified specialists could reduce the potential for human error and minimize the risk of injury to inexperienced staff.

But if on-site staff were effectively designed out of facilities, who or what would replace them? The kind of pervasive remote monitoring platforms already used at lights-out sites -- such as EdgeConneX’s edgeOS -- would likely play an instrumental role. Emerging tools, such as data center management as a service (DMaaS), which is effectively cloud-based data center infrastructure management, or DCIM, software – could also enable suppliers to take remote control (including predictive maintenance) of specific equipment or even an entire site. Eventual integration with AI/machine learning could also lead to more IT and facilities tasks being automated and self-regulated. Robotics is also likely to play a greater role in future data center management. Indeed, if facilities are designed to optimize space, then so-called dexterous robots may be the only way to access some parts of the site.

But despite the potential, a number of impediments will need to be overcome before unstaffed data centers become widely adopted. The biggest of these is obviously the perception that such designs would introduce additional risk. As such, early adopters would probably be limited to companies that are already comfortable with some form of lights-out approach. Facilitating technologies, such as DMaaS, AI-driven DCIM, and advanced robotics, are also still very nascent.

But there are still good reasons to think that, in specific use cases, unstaffed sites will eventually become the norm. For example, new micro-data center form factors to support edge computing are expected to proliferate in the next five to ten years and are likely to be monitored remotely and only require periodic visits from specialist maintenance staff.

Ihe prognosis doesn’t necessarily have to be all bad for facilities staff. To be sure, there will be fewer in-house positions in the future, but specialist third-party facilities management services providers – capable of emergency or periodic visits -- could expand headcount to meet the expected growth in new colocation and cloud capacity.

Ironic as it may sound, the future looks rather bright for the next generation of lights-out data centers.

October 2, 201711:09 am PTORLANDO, Florida – Big numbers drew applause to what are typically rather staid affairs at the Microsoft Ignite event last week.

During a panel session entitled: “Orchestrating 1 million containers with Azure Service Fabric,” Mani Ramaswamy, principal program manager at Microsoft, did indeed show the creation and orchestration of one million containers. Even more impressive was that the demonstration took less than two minutes to complete.

Though this drew audience applause from what are typically sleepy afternoon sessions on the last real day of the conference, Ramaswamy seemed to want a bit more.

“I expected dancing in the aisles,” Ramaswamy joked (or at least it seemed like he was joking). He added that the more impressive part of the platform was that it was able to hold the reliability and availability of the instances at hyperscale.

“You never again have to worry about whether the platform can meet scale demands,” he said. “It’s the application that you have to worry about, not the platform.”

A container instance is a single container that is designed to start within seconds and can be billed by the provider in second increments. That billing typically includes the cost of turning up an instance, and charges for the processing and memory needed to run the instance.

Containers can run with a public or private IP address, with the former able to support consumer services accessed via the Internet, and the latter typically used for internal processes.

Ramaswamy said some of Microsoft’s competitors have been able to show public demonstrations of “a few hundred thousand” container instances created. Those rivals would seem to include Amazon, which has its ECS container instance.

The demonstration was the crescendo to Ramaswamy’s presentation on the flexibility and capabilities of Microsoft’s Azure Service Fabric.

Microsoft, during the show, launched general availability of its Azure Service Fabric on Linux. The product is a platform-as-a-service (PaaS) that supports running containerized applications on Service Fabric for Windows Server and Linux.

Developers can manage container images, allocate resources, run service discovery, and tap insight from operation management suite (OMS) integration. This work can then be ported between Windows Server and Linux without needing to alter code.

While the product can support both Windows and Linux, it can’t support both at the same time. Ramaswamy said Microsoft was looking to add that form of support in the coming months.

Data-center operator Switch Inc. SWCH, +0.00% priced its initial public offering higher than expected Thursday evening to pull in more than half a billion dollars. The Las Vegas-based data-center company, which owns three large data centers and is developing a fourth, announced that it would sell 31.25 million shares at $17 apiece, after previously stating a target range of $14 to $16. At that price, Switch stands to collect at least $531.25 million at a valuation of about $4.2 billion; underwriters have access to another 4.7 million shares, which could push the take even higher. The company has said it will use the proceeds to buy out investors in Switch Ltd. and take control of that company though Switch Inc., which was just incorporated in 2017. A multi-class share structure will allow founder and Chief Executive Rob Roy to maintain control, as his shares will have 10 times the voting rights of common shares. Switch is expected to begin trading Friday morning on the New York Stock Exchange under the ticker symbol SWCH.

The tech giants with cloud computing businesses are using artificial intelligence offerings to distinguish themselves and win business.

By Fast Company Staff

10.11.1712:30 pm

The success of Amazon’s Alexa voice assistant has reverberated throughout the business world, making AI- powered chat the next big thing. [Illustration: Daniel Zender] _____________________

Alphabet, Amazon, and Microsoft have all discovered that the artificial intelligence they use to make their own products better can be turned into a service and sold to corporate customers as a value-added service on top of their booming cloud-computing businesses.

Alphabet and its best-known subsidiary, Google, have put considerable resources into machine learning going back to 1999, the first year that Google acknowledged publicly that it used AI to improve Google Search, then its only product. Once Google decided to get more serious about its cloud computing business and serving enterprise customers—Google Cloud storage officially launched in 2010—it has found more ways to take its AI investment and acumen and use it to serve others. Diane Greene, SVP of Google Cloud, has admitted that enterprise customers had been wary of Google because the company has been so consumer focused; its AI capabilities have played a meaningful role in winning them over.

Alphabet has two major divisions working on AI: Google Brain and DeepMind, which it acquired for $500 million in 2014. Both groups have worked on applying AI in healthcare, for example, which then allows Google Cloud to better serve businesses in that field. The company’s efforts in image recognition can become valuable for Airbus and other aerospace businesses that need to process and glean insights from large volumes of satellite imagery. All of Google’s work on Google Translate can now help any global business with a call center. Although most of the value in Google’s AI accrues to its own products and services, the company has stated that Google Cloud is one of its fastest-growing business units.

Amazon has a much more natural synergy between its AI efforts and how it can sell those initiatives to others via its industry-leading cloud computing service. As CEO Jeff Bezos wrote earlier this year in his letter to shareholders, “Much of what we do with machine learning happens beneath the surface . . . quietly but meaningfully improving core operations.” The examples Bezos cites include demand forecasting, fraud detection, and translations—all features that any business would value. As our feature on the Great AI War recounts, a sheriff’s department in Oregon pays Amazon about $6 a month to use Amazon’s facial-recognition service on an ongoing basis.

More than any of its rivals, Amazon has electrified the public with its audacious vision for an AI-powered future. Its line of Echo devices, brought to life by the artificially intelligent Alexa, has defined the path for the next generation of home automation and commerce and made voice-powered speakers arguably the hottest segment in consumer electronics. That success has enabled Amazon to release the technology powering Alexa as its own product so that any company can develop its own intelligent voice applications.

This strategy is central to Amazon’s history of success; it has largely always relied upon its ability to transform something it built for itself into something it can then sell to millions of businesses. Amazon started as a mere bookseller and then opened up its marketplace to let other retailers take advantage of its e-commerce platform. After it built warehouses to fulfill orders for customers, it offered Fulfillment by Amazon to those same marketplace businesses. Amazon Web Services started because Amazon had had to build excess computing capacity to support its business during the busiest shopping season; it could then sell that capacity to a host of others. This is how Amazon’s famous “flywheel” works and AI-powered services are its next frontier.

To that end, keep a close eye on the company’s retail concept called Go. It relies on computer vision and machine learning to present a different kind of shopping experience. Amazon has yet to open this new take on the convenience store to the public almost a year after announcing the idea. But once the company gets Go working, do not expect the company to roll out thousands of Go stores across the country. It is far more likely that Amazon will offer up this AI-powered retail infrastructure to existing shopkeepers who will pay Amazon a recurring fee to use it.

Also note that Amazon Web Services currently represents almost 10% of the company’s annual revenue and it is a part of Amazon’s business that investors monitor very closely. The more Amazon can keep AWS humming, the more its entire enterprise thrives.

Unlike Alphabet/Google or Amazon, almost all of Microsoft’s business lies in serving enterprise customers. It is the tech giant most focused on converting AI directly into revenue. “Our company’s identity is fundamentally about creating technology so that others can create more technology,” CEO Satya Nadella told Fast Company recently. “And it’s essential that it is being used for empowering more people.”

Artificial intelligence “is at the intersection of our ambitions,” Nadella told an audience of Microsoft partners in September 2016, suggesting that it will let the company “reason over large amounts of data and convert that into intelligence.” A few months later, Microsoft officially closed its $26.2 billion acquisition of LinkedIn, giving the company a large amount of data about employees, companies, and recruiting to reason over and try to make smarter.

In August, it debuted a real-time AI system for its enterprise cloud customers, which could help the company win business from companies who want to deploy such business initiatives as dynamic pricing and retail personalization. Microsoft’s mission to help companies in a wide range of industries to be more productive and effective means that it is the one company whose AI work is most keenly connected to its future prospects.

Similarly, IBM’s approach has been to target specific industries, from healthcare to retail, and learn those domains so that its Watson-branded AI (which IBM calls cognitive computing) can alleviate drudge work and wrangle impossibly large sets of data. “There’s a reason we call it cognitive [computing],” IBM CEO Ginni Rometty told the CNBC personality Jim Cramer in June 2017. “It’s about augmenting what you and I do so we can do what we’re supposed to, our best.”

IBM’s argument to customers is that it is the only company offering sector-based AI solutions and those businesses within them can own their own AI rather than just rent it. It’s also made the most overt effort to connect its industrial internet of things initiative to Watson, as best seen in IBM’s 2016 acquisition of The Weather Company for approximately $2 billion. The deal gave IBM access to 2.2 billion forecast points worldwide, a trove of data that Watson churns through to fuel multiple client services. These efforts have generated a lot of attention and Watson is arguably the strongest brand in AI, but they haven’t yet turned around IBM’s business.

A version of this article appeared in the November 2017 issue of Fast Company magazine.